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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring
Timothy J. Rogers, Keith Worden, R. Fuentes, et al.
Mechanical Systems and Signal Processing (2018) Vol. 119, pp. 100-119
Open Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019
Rongrong Hou, Yong Xia
Journal of Sound and Vibration (2020) Vol. 491, pp. 115741-115741
Open Access | Times Cited: 466
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Arman Malekloo, Ekin Özer, Mohammad AlHamaydeh, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 4, pp. 1906-1955
Open Access | Times Cited: 320
The Current Development of Structural Health Monitoring for Bridges: A Review
Z.C. Deng, Minshui Huang, Neng Wan, et al.
Buildings (2023) Vol. 13, Iss. 6, pp. 1360-1360
Open Access | Times Cited: 94
Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
Hassan Sarmadi, Ka‐Veng Yuen
Mechanical Systems and Signal Processing (2022) Vol. 173, pp. 109049-109049
Closed Access | Times Cited: 70
Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning
Alireza Entezami, Hassan Sarmadi, Bahareh Behkamal
Engineering Structures (2023) Vol. 279, pp. 115616-115616
Open Access | Times Cited: 64
On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method
Alireza Entezami, Hassan Sarmadi, Bahareh Behkamal, et al.
Structure and Infrastructure Engineering (2023) Vol. 20, Iss. 12, pp. 1975-1993
Closed Access | Times Cited: 41
Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects
Ali Zar, Zahoor Hussain, Muhammad Akbar, et al.
International Journal of Mechanics and Materials in Design (2024) Vol. 20, Iss. 3, pp. 591-662
Open Access | Times Cited: 15
Sensor placement algorithm for structural health monitoring with redundancy elimination model based on sub-clustering strategy
Chen Yang, Ke Liang, Xuepan Zhang, et al.
Mechanical Systems and Signal Processing (2019) Vol. 124, pp. 369-387
Closed Access | Times Cited: 141
Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes
Yang Yang, Yao Zhang, Xiaokun Tan
Symmetry (2021) Vol. 13, Iss. 11, pp. 1998-1998
Open Access | Times Cited: 100
Eliminating environmental and operational effects on structural modal frequency: A comprehensive review
Zhen Wang, Dong‐Hui Yang, Ting‐Hua Yi, et al.
Structural Control and Health Monitoring (2022) Vol. 29, Iss. 11
Open Access | Times Cited: 67
Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform
Zuoyi Chen, Yanzhi Wang, Jun Wu, et al.
Applied Intelligence (2021) Vol. 51, Iss. 8, pp. 5598-5609
Closed Access | Times Cited: 64
Probabilistic data self-clustering based on semi-parametric extreme value theory for structural health monitoring
Hassan Sarmadi, Alireza Entezami, Carlo De Michele
Mechanical Systems and Signal Processing (2022) Vol. 187, pp. 109976-109976
Closed Access | Times Cited: 53
Toward a general unsupervised novelty detection framework in structural health monitoring
Mohammad Hesam Soleimani‐Babakamali, Reza Sepasdar, Kourosh Nasrollahzadeh, et al.
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 37, Iss. 9, pp. 1128-1145
Closed Access | Times Cited: 45
Probabilistic active learning: An online framework for structural health monitoring
Lawrence A. Bull, Timothy J. Rogers, Chandula T. Wickramarachchi, et al.
Mechanical Systems and Signal Processing (2019) Vol. 134, pp. 106294-106294
Open Access | Times Cited: 57
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx, Wannes Meert, Yves Mollet, et al.
Mechanical Systems and Signal Processing (2020) Vol. 139, pp. 106585-106585
Open Access | Times Cited: 51
Machine learning at the interface of structural health monitoring and non-destructive evaluation
Paul Gardner, R. Fuentes, Nikolaos Dervilis, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2020) Vol. 378, Iss. 2182, pp. 20190581-20190581
Open Access | Times Cited: 51
Semi-supervised vibration-based structural health monitoring via deep graph learning and contrastive learning
Viet-Hung Dang, Khuong Le‐Nguyen, Truong-Thang Nguyen
Structures (2023) Vol. 51, pp. 158-170
Closed Access | Times Cited: 18
Data-Driven Monitoring and Predictive Maintenance for Engineering Structures: Technologies, Implementation Challenges, and Future Directions
Qianyi Chen, Jiannong Cao, Songye Zhu
IEEE Internet of Things Journal (2023) Vol. 10, Iss. 16, pp. 14527-14551
Closed Access | Times Cited: 16
Unsupervised deep learning approach for structural anomaly detection using probabilistic features
Hua‐Ping Wan, Yi-Kai Zhu, Yaozhi Luo, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 6
Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data
Lawrence A. Bull, Keith Worden, R. Fuentes, et al.
Journal of Sound and Vibration (2019) Vol. 453, pp. 126-150
Open Access | Times Cited: 52
Tunnel condition assessment via cloud model‐based random forests and self‐training approach
Mengqi Zhu, Hehua Zhu, Feng Guo, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 36, Iss. 2, pp. 164-179
Closed Access | Times Cited: 49
A probabilistic risk-based decision framework for structural health monitoring
Aidan J. Hughes, R. J. Barthorpe, Nikolaos Dervilis, et al.
Mechanical Systems and Signal Processing (2020) Vol. 150, pp. 107339-107339
Open Access | Times Cited: 49
Model-free data reconstruction of structural response and excitation via sequential broad learning
Sin‐Chi Kuok, Ka‐Veng Yuen
Mechanical Systems and Signal Processing (2020) Vol. 141, pp. 106738-106738
Closed Access | Times Cited: 45
A transfer Bayesian learning methodology for structural health monitoring of monumental structures
Laura Ierimonti, Nicola Cavalagli, Ilaria Venanzi, et al.
Engineering Structures (2021) Vol. 247, pp. 113089-113089
Open Access | Times Cited: 39
On risk-based active learning for structural health monitoring
Aidan J. Hughes, Lawrence A. Bull, Paul Gardner, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108569-108569
Open Access | Times Cited: 34
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